A Combinatorial Approach to Designing RNA Secondary Structures and DNA Code Words
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In the nucleus, lengthy DNA molecules have a canonical double-stranded helix structure well-adapted for information storage and retrieval. In the laboratory, short single-stranded DNA sequences have many possible applications, ranging from microarrays to nanomolecular structures and DNA computation. Furthermore, since recent discoveries have highlighted the many vital regulatory and catalytic functions performed by different RNA molecules, other than mediating the production of proteins from DNA, increasing interest is focused on the self-bonding of RNA molecules and on the design, analysis, and prediction of RNA secondary structures.
With the goal of understanding the biological information encoded by the selective base pair hybridization of single-stranded DNA and RNA molecules, we develop algorithms for the design of RNA secondary structures and DNA code words based on discrete mathematical models. Our combinatorial approach relies on reducing the problem of designing an RNA sequence with a specified set of base pairings to the question of creating a set of nucleotide code words of sufficient quality. Thus, the essential challenge in both cases is producing sets of short oligonucleotides whose whose elements are strongly differentiated from each other with respect to the biochemical energetics.
Dr. Christine Heitsch is currently Associate Director of a new institute for computational biology research and education through the Mathematics Department at the University of Wisconsin – Madison. Dr. Heitsch received her Ph.D. in Mathematics from the University of California at Berkeley in 2000. Subsequently, she was a postdoc in theoretical computer science at the University of British Columbia and a computational biology postdoc at Madison, affiliated with the mathematics, computer sciences, and chemistry departments. In keeping with her institutional affiliations, Dr. Heitsch's research focuses on combinatorics and discrete mathematics as motivated by and with applications to theoretical computer science and computational biology.